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Risk Factors Associated With Musculoskeletal Injury: A Prospective Study of British Infantry Recruits
Jagannath Sharma1,2,3, Robert Heagerty1, S Dalal4
1Medical Centre & Rehabilitation Department, Defence Primary Healthcare, Infantry Training Centre Catterick Garrison, DL9 3PS, United Kingdom.
Background:
Musculoskeletal Injury (MSKI), a common problem in both military and physically active civilian populations, has been suggested to result from both extrinsic and intrinsic factors.
Objective:
To investigate prospectively whether gait biomechanics, aerobic fitness levels and smoking status as well as entry military selection test variables can be used to predict MSKI development during recruit training.
Methods:
British infantry male recruits (n = 562) were selected for the study. Plantar pressure variables, smoking habit, aerobic fitness as measured by a 1.5 mile run time and initial military selection test (combination of fitness, Trainability score) were collected prior to commencement of infantry recruit training. Injury data were collected during the 26 week training period.
Results:
Incidence rate of MSKI over a 26 week training period was 41.28% (95 % CI: 37.28 - 45.40%). The injured group had a higher medial plantar pressure (p < 0.03), shorter time to peak heel rotation (p < 0.02), current smoking status (p < 0.001) and a slower 1.5 mile run time (p < 0.03). In contrast, there were no significant differences (p > 0.23) in lateral heel pressure, age, weight, height, BMI and military selection test. A logistic regression model predicted MSKI significantly (p= 0.03) with an accuracy of 34.50% of all MSK injury and 76.70% of the non-injured group with an overall accuracy of 69.50%.
Conclusion:
The logistic regression model combining the three risk factors was capable of predicting 34.5% of all MSKI. A specific biomechanical profile, slow 1.5 mile run time and current smoking status were identified as predictors of subsequent MSKI development. The proposed model could include evaluation of other potential risk factors and if validated then further enhance the specificity, sensitivity and applicability.
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